A Feasibility Study on Noninvasive Blood Glucose Estimation Using Machine Learning Analysis of Near-Infrared Spectroscopy Data

TL;DR

This study tested using near-infrared light to noninvasively estimate blood glucose levels in dogs, finding promising results at specific wavelengths.

Contribution

The study introduces a new NIR–glucose database from dog blood and identifies optimal wavelengths for noninvasive glucose sensing.

Findings

Negative correlations at 850 nm and 970 nm suggest these wavelengths are effective for glucose sensing.

An NIR–glucose database was created using actual dog blood for future noninvasive monitoring research.

Abstract

This study explored the feasibility of noninvasive blood glucose (BG) estimation using near-infrared (NIR) spectroscopy with dog blood samples. A sensor module employing three representative wavelengths (770 nm, 850 nm, and 970 nm) was tested on an artificial blood vessel (ABV) and a thin pig skin (TPS) model. BG concentrations were adjusted through dilution and enrichment with injection-grade water and glucose solution, and reference values were obtained from three commercial invasive glucometers. Correlations between NIR spectral responses and glucose variations were quantitatively evaluated using linear, multiple, partial least squares (PLS), logistic regression, regularized linear models, and multilayer perceptron (MLP) analysis. The results revealed distinct negative correlations at 850 nm and 970 nm, identifying these wavelengths as promising candidates for noninvasive glucose sensing. Furthermore, an NIR–glucose database generated from actual dog blood was established, which may serve as a valuable resource for the development of future noninvasive glucose monitoring systems.

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